📄 Ensemble V1 Ibmgenai Judges¶
A pre-trained Random Forest-based ensemble judge to assesss multi-turn conversation quality on answer groundedness: Every substantial claim in the response of the model is derivable from the content of the document.
metrics.llm_as_judge.conversation_answer_groundedness.ensemble_v1_ibmgenai_judges
RandomForestMetricsEnsemble(
metrics=[
LLMAsJudge(
inference_model=IbmGenAiInferenceEngine(
model_name="ibm/granite-13b-instruct-v2",
parameters=IbmGenAiInferenceEngineParams(
max_new_tokens=256,
),
),
template="templates.response_assessment.judges.grounded.v8",
task="rating.single_turn",
main_score="metrics.llm_as_judge.rating.granite13binstructv2_template_v8",
prediction_type="str",
),
LLMAsJudge(
inference_model=IbmGenAiInferenceEngine(
model_name="meta-llama/llama-3-70b-instruct",
parameters=IbmGenAiInferenceEngineParams(
max_new_tokens=256,
),
),
template="templates.response_assessment.judges.grounded.v10",
task="rating.single_turn",
main_score="metrics.llm_as_judge.rating.llama370binstruct_template_v10",
prediction_type="str",
),
LLMAsJudge(
inference_model=IbmGenAiInferenceEngine(
model_name="mistralai/mixtral-8x7b-instruct-v01",
parameters=IbmGenAiInferenceEngineParams(
max_new_tokens=256,
),
),
template="templates.response_assessment.judges.grounded.v10",
task="rating.single_turn",
main_score="metrics.llm_as_judge.rating.mixtral8x7binstructv01_template_v10",
prediction_type="str",
),
LLMAsJudge(
inference_model=IbmGenAiInferenceEngine(
model_name="meta-llama/llama-3-70b-instruct",
parameters=IbmGenAiInferenceEngineParams(
max_new_tokens=256,
),
),
template="templates.response_assessment.judges.grounded.v11",
task="rating.single_turn",
main_score="metrics.llm_as_judge.rating.llama370binstruct_template_v11",
prediction_type="str",
),
LLMAsJudge(
inference_model=IbmGenAiInferenceEngine(
model_name="mistralai/mixtral-8x7b-instruct-v01",
parameters=IbmGenAiInferenceEngineParams(
max_new_tokens=256,
),
),
template="templates.response_assessment.judges.grounded.v11",
task="rating.single_turn",
main_score="metrics.llm_as_judge.rating.mixtral8x7binstructv01_template_v11",
prediction_type="str",
),
LLMAsJudge(
inference_model=IbmGenAiInferenceEngine(
model_name="ibm/granite-13b-chat-v2",
parameters=IbmGenAiInferenceEngineParams(
max_new_tokens=256,
),
),
template="templates.response_assessment.judges.grounded.v11",
task="rating.single_turn",
main_score="metrics.llm_as_judge.rating.granite13bchatv2_template_v11",
prediction_type="str",
),
],
weights={
"judges": [
[
"templates.response_assessment.judges.grounded.v8",
"ibm/granite-13b-instruct-v2",
],
[
"templates.response_assessment.judges.grounded.v10",
"meta-llama/llama-3-70b-instruct",
],
[
"templates.response_assessment.judges.grounded.v10",
"mistralai/mixtral-8x7b-instruct-v01",
],
[
"templates.response_assessment.judges.grounded.v11",
"meta-llama/llama-3-70b-instruct",
],
[
"templates.response_assessment.judges.grounded.v11",
"mistralai/mixtral-8x7b-instruct-v01",
],
[
"templates.response_assessment.judges.grounded.v11",
"ibm/granite-13b-chat-v2",
],
],
"platform_name": "ibm_gen_ai",
"metric_name": "grounded",
"version_number": "v1",
"ensemble_model_name": "random_forest",
"max_depth": 100,
"min_samples_split": 2,
"min_samples_leaf": 1,
"min_weight_fraction_leaf": 0.0,
"max_features": "sqrt",
"max_leaf_nodes": None,
"min_impurity_decrease": 0.0,
"n_features_in_": 6,
"feature_names_in_": [
"v8_ICL1_granite-13b-v2",
"v10_ICL1_llama-3-70b",
"v10_ICL1_mixtral-instruct-v0-1",
"v11_ICL1_llama-3-70b",
"v11_ICL1_mixtral-instruct-v0-1",
"v11_ICL1_lab-13b-v2",
],
"n_outputs_": 1,
"classes_": [
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],
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"max_features_": 2,
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[source]from unitxt.inference import IbmGenAiInferenceEngine, IbmGenAiInferenceEngineParams
from unitxt.llm_as_judge_from_template import LLMAsJudge
Explanation about RandomForestMetricsEnsemble¶
This class extends the MetricsEnsemble base class and leverages a pre-trained scikit-learn Random Forest classification model to combine and aggregate scores from multiple judges.
- load_weights method:
Loads model weights from dictionary representation of a random forest classifier.
- ensemble method:
Decodes the RandomForestClassifier object and predict a score based on the given instance.
Explanation about LLMAsJudge¶
LLM-as-judge-based metric class for evaluating correctness of generated predictions.
This class uses the source prompt given to the generator and the generator’s predictions to evaluate correctness using one of three supported tasks (rating.single_turn, rating.single_turn_with_reference, pairwise_comparative_rating.single_turn).
- Attributes:
main_score (str): The main score label used for evaluation.
task (Literal[“rating.single_turn”,”rating.single_turn_with_reference”, “pairwise_comparative_rating.single_turn”]): The type of task the llm as judge runs. This defines the output and input format of the judge model.
template (Template): The template used when generating inputs for the judge llm.
format (Format): The format used when generating inputs for judge llm.
system_prompt (SystemPrompt): The system prompt used when generating inputs for judge llm.
strip_system_prompt_and_format_from_inputs (bool): Whether to strip the system prompt and formatting from the inputs that the models that is being judges received, when they are inserted to the llm-as-judge prompt.
inference_model (InferenceEngine): The module that creates the inference of the judge llm.
reduction_map (dict): A dictionary specifying the reduction method for the metric.
batch_size (int): The size of the bulk.
References: templates.response_assessment.judges.grounded.v10, templates.response_assessment.judges.grounded.v11, templates.response_assessment.judges.grounded.v8
Read more about catalog usage here.